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  Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking

Zhou, D. (2004). Learning from Labeled and Unlabeled Data: Semi-supervised Learning and Ranking. Talk presented at -. The Natural Language Computing Group of Microsoft Research Asia, and the Institute of System Sciences, the Chinese Academy of Sciences, Beijing, China.

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 Urheber:
Zhou, D1, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.

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 Datum: 2004-01
 Publikationsstatus: Erschienen
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 Identifikatoren: BibTex Citekey: 2589
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Titel: -
Veranstaltungsort: The Natural Language Computing Group of Microsoft Research Asia, and the Institute of System Sciences, the Chinese Academy of Sciences, Beijing, China
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